Predictive Analytics Analysis

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Introduction
Predictive analytics is an advanced analytics technique. Predective analytics uses both new and historical data to foresee the result, activity, behavior and trends.

Statistics is branch of mathematics, mainly concerns about collection, analysis, interpretation and presentation of tons of numerical facts. Statistics is used in almost every field of research.

Head to Head Comparisons Predictive Analytics Statistics

Definition Predictive analytics is branch of the data analytics to predict the future events. Statistics in simpler terms is collection of numerical facts. It is the science of collecting, classifying and representing the numerical data.

Why it matters?
Predictive analytics can identify the risks and opportunities
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Branches Predective analytics is one of the types of Data Analytics. The other analytics are descriptive and prescriptive analytics. The two main branches of statistics are descriptive statistics and inferential statistics.

Key differences:
• Predective Analytics is used to make predictions about unknown future events. Whereas statistics is the science and it’s mainly used in ‘Research’. Statistics helps in making conclusion from the data by collecting, analyzing and presenting.

• For a business to bloom, it must collect and generate facts that reflect its current status. Statistics helps these facts or data to be changed into information, in order to support rational management decision making.

How it works:

• In Predective Analytics, predictive models use known results to develop or train a model that can be used to predict values for different or new data. This modeling provides results in the form of predictions that represent a probability of the target variable based on estimated importance from a set of input variables.

• Statistics summarizes the data for public use. There are two main statistical methods: Descriptive Statistics and Inferential
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• Big companies are using predictive analytics. For example open your Amazon site and take a look around the site. A huge percentage of the screen is devoted to “recommended” products, and each recommendation area is a slightly different predictive algorithm based on different data.
These recommendations are based on the search history of the items that user browsed, based on that a model will be trained, so when user opens the site he/she will be seeing all the relent items to the previous product that he/she browsed. It could be a brand of the product or the color or the style of the product. That strategy is to attract and engage the user on the website and obviously to by the products.
Here in the above example statistics will help to develop the prediction model, using its descriptive and inferential models.

Conclusion: Predective ‘Analytics’ and ‘Statistics’ are useful to analyze current data and historical data to make predictions about future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning and artificial

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